• Journal of the Korea Concrete Institute
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• v.23 no.3
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• pp.295-301
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• 2011

Recently, the use of UHPC made of superplasticizers, silica fume, and steel fibers has been increasing worldwide. Although UHPC has a very high strength as well as an excellent durability performance due to its dense microstructures, earlyage cracks may occur due to the high heat of hydration and autogenous shrinkage caused by low W/B and high unit cement content. The early-age shrinkage cracking of UHPC can be controlled by using the shrinkage reducers and expansive admixtures having autogenous shrinkage compensation effect. In this paper, ultrasonic pulse velocity of UHPC containing shrinkage reducers and expansive agents was measured to predict its stiffness change. Also, the effect of shrinkage reducers and expansive agents on the autogenous shinkage of UHPC was investigated through the shrinkage test of UHPC specimens. Furthermore, the material coefficients of autogenous shrinkage prediction model were determined using the autogenous shrinkage values of UHPC with age. Consequently, the test results showed that, by adding shrinkage reducers and expansive agents, the stiffness of UHPC was rapidly developed at early-ages and the autogenous shrinkage was considerably reduced.

• Elastomers and Composites
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• v.47 no.2
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• pp.129-140
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• 2012

The objective of this study was to estimate the practical recycling rate of plastic products, so that the study was conducted to build material flow diagram for PVC profile. For this objective, product generation, waste generation and recycling status were investigated. Using collected and analyzed status data, analysis of material flow by product and building material flow diagram were conducted. As result of estimating the recycling rate by product, The sum of domestic demand was 525,448 ton and waste generation was 105,853ton in PVC flooring and profile. The sum of generation of recycling product and raw material was investigated to be 76,004ton(14.46%), which is higher compared to recycling obligation(8.5%) in 2009. To build the material flow diagram in the years(5~20years) ahead, prediction of future demand was based on the assumption that there will be no difference in annual generation of current and future. As the recycling rate of flooring and profile increases, it is estimated to reach 20% in 2013 according to the material flow diagram.

• Journal of the Korean Recycled Construction Resources Institute
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• v.7 no.2
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• pp.109-115
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• 2019

In case of the cracks in concrete, the penetration of deterioration ions such as chloride ions in to cracks is accelerated. According to the penetration of chloride ions, structural and durability problems to RC(Reinforced Concrete) structures are caused. In this study, the accelerated chloride diffusion coefficient which is in steady state is evaluated for 2 year aged normal and high strength FA(Fly Ash) concrete, after a range of crack depths are induced up to 1.0 mm in 56 aged day. Considering crack effect by linear regression analysis, high strength concrete has slightly less increasing ratio of diffusion coefficient by crack than normal strength concrete, and diffusion coefficient increases non-linearly as crack width is increased. Also, In two types of concrete, crack effect decrease as the curing period increase. In the case of quantifying crack and curing effect by using exponential function form, the coefficients of determination are higher than those of linear regression analysis. Under steady state, it is thought that there is not a high correlation between the crack effect and the curing effect, and considering the two independent effects, it is believed that reasonable prediction equation for diffusion of concrete with crack can be proposed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.833-835
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• 2014

On this study, we have developed respiratory training system to improve stability of respiration, one of the most important factors of Respiratory Gated Radiation Therapy, RGRT. Respiratory training system that we developed was applied to personal respiratory cycle so that it could provide comfortable respiratory triggering to patients. To give sufficient time for practice, we used modular portable device to practice easily and to be undetered by time and place. We have intended to improve efficiency and accuracy by providing it to patients. We are now planning to conduct experiment of 10 peoples to find out stability, degree of durability betterment and regularity of respiration when patients are using respiratory training system. There are three kinds of breathing style. First is free breathing that Individual patients can breathe freely. Second is guide breathing that patients apply to personal respiration cycle through the guiding sight and hearing program. Third is prediction breathing that patients breathe after respiratory training without guiding sight and hearing program. By using these 3 data of respiration method, we have evaluated usability of respiratory training system by quantitatively analyzing respiration period, amplitude and area's variation.

• Journal of the Korean Geotechnical Society
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• v.37 no.8
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• pp.25-35
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• 2021

Recently, the ground anchor method is commonly applied with nail and rock bolt to secure the stability of slopes and structures in Korea. Among them, permanent anchor which is used for long-term stability should secure bearing capacity and durability during the period of use. However, according to recent studies, phenomenon such as deformation to slope and the reduction of residual tensile load over time have been reported along the long-term behavior of the anchors. These problems of reducing residual tensile load are expected to increase in the future, which will inevitably lead to problems such as increasing maintenance costs. In this study, we identified the factors that affect the tensile load of permanent anchor from a literature study on the domestic and foreign, and investigated the prior studies that analyzed previously conducted load cell monitoring data. Afterwards, using this as basic data, the load cell measurement data collected at the actual site were analyzed to identify the tensile load reduction status of anchors, and the long-term load reduction characteristics were analyzed. Finally, by aggregating the preceding results, proposed a technique to predict the long-term load reduction characteristics of permanent anchors through short-term data to around 100 days after installation.

• Journal of the Korean Institute of Gas
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• v.26 no.6
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• pp.9-15
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• 2022

In order to verify the durability of the high-pressure hydrogen tank in the operating pressure range, a hydraulic rupture test should be performed. However, if the bubbles generated by the initial injection process of water are attached to the inner wall of the tank and remain, a sudden pressure change of the bubbles during the rupture of the pressurized tank may cause shock and noise. Therefore, in this study, the flow velocity required to remove the bubbles remaining on the inner wall of the tank was predicted through simplified formulas, and the shape of the injection nozzle to maintain the flow velocity was determined based on the shape of the hydrogen tank for the hydrogen bus. In addition, a numerical model was developed to predict the change in flow velocity according to the inlet pressure, and an experiment was performed through a model tank to prove the validity of the prediction result. As a result of the experiment, the flow velocity near the tank wall was similar to the predicted value of the analysis model, and when the inlet pressure was 1.5 to 5.5 bar, the minimum size of the removable bubble was predicted to be about 2.2 to 4.6 mm.

• Wind and Structures
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• v.36 no.4
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• pp.237-247
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• 2023

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.

• Steel and Composite Structures
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• v.50 no.6
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• pp.705-720
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• 2024

Analyzing the collapse behavior of thin-walled steel structures holds significant importance in ensuring their safety and longevity. Geometric imperfections present on the surface of metal materials can diminish both the durability and mechanical integrity of steel shells. These imperfections, encompassing local geometric irregularities and deformations such as holes, cavities, notches, and cracks localized in specific regions of the shell surface, play a pivotal role in the assessment. They can induce stress concentration within the structure, thereby influencing its susceptibility to buckling. The intricate relationship between the buckling behavior of these structures and such imperfections is multifaceted, contingent upon a variety of factors. The buckling analysis of thin-walled steel shell structures, similar to other steel structures, commonly involves the determination of crucial material properties, including elastic modulus, shear modulus, tensile strength, and fracture toughness. An established method involves the emulation of distributed geometric imperfections, utilizing real test specimen data as a basis. This approach allows for the accurate representation and assessment of the diversity and distribution of imperfections encountered in real-world scenarios. Utilizing defect data obtained from actual test samples enhances the model's realism and applicability. The sizes and configurations of these defects are employed as inputs in the modeling process, aiding in the prediction of structural behavior. It's worth noting that there is a dearth of experimental studies addressing the influence of geometric defects on the buckling behavior of cylindrical steel shells. In this particular study, samples featuring geometric imperfections were subjected to experimental buckling tests. These same samples were also modeled using Finite Element Analysis (FEM), with results corroborating the experimental findings. Furthermore, the initial geometrical imperfections were measured using digital image correlation (DIC) techniques. In this way, the response of the test specimens can be estimated accurately by applying the initial imperfections to FE models. After validation of the test results with FEA, a numerical parametric study was conducted to develop more generalized design recommendations for the stainless-steel shell structures with the initial geometric imperfection. While the load-carrying capacity of samples with perfect surfaces was up to 140 kN, the load-carrying capacity of samples with 4 mm defects was around 130 kN. Likewise, while the load carrying capacity of samples with 10 mm defects was around 125 kN, the load carrying capacity of samples with 14 mm defects was measured around 120 kN.

• Journal of Technology Innovation
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• v.26 no.3
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• pp.37-68
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• 2018

Since the 20th century, automobiles, which are the most common means of transportation, have been evolving as the use of electronic control devices and automotive semiconductors increases dramatically. Automotive semiconductors are a key component in automotive electronic control devices and are used to provide stability, efficiency of fuel use, and stability of operation to consumers. For example, automotive semiconductors include engines control, technologies for managing electric motors, transmission control units, hybrid vehicle control, start/stop systems, electronic motor control, automotive radar and LIDAR, smart head lamps, head-up displays, lane keeping systems. As such, semiconductors are being applied to almost all electronic control devices that make up an automobile, and they are creating more effects than simply combining mechanical devices. Since automotive semiconductors have a high data rate basically, a microprocessor unit is being used instead of a micro control unit. For example, semiconductors based on ARM processors are being used in telematics, audio/video multi-medias and navigation. Automotive semiconductors require characteristics such as high reliability, durability and long-term supply, considering the period of use of the automobile for more than 10 years. The reliability of automotive semiconductors is directly linked to the safety of automobiles. The semiconductor industry uses JEDEC and AEC standards to evaluate the reliability of automotive semiconductors. In addition, the life expectancy of the product is estimated at the early stage of development and at the early stage of mass production by using the reliability test method and results that are presented as standard in the automobile industry. However, there are limitations in predicting the failure rate caused by various parameters such as customer's various conditions of use and usage time. To overcome these limitations, much research has been done in academia and industry. Among them, researches using data mining techniques have been carried out in many semiconductor fields, but application and research on automotive semiconductors have not yet been studied. In this regard, this study investigates the relationship between data generated during semiconductor assembly and package test process by using data mining technique, and uses data mining technique suitable for predicting potential failure rate using customer bad data.